A data-driven optimal control method based on Pontryagin Maximum Principles.

Quadratic programming solvers in Python with a unified API

Deep reinforcement learning based energy management strategy for hybrid electric vehicle

Reinforcement Learning for Solving the Vehicle Routing Problem

VED (Vehicle Energy Dataset): A Large-scale Dataset for Vehicle Energy Consumption Research. (IEEE Transactions on Intelligent Transportation Systems, 2020)

Code for "Deep Reinforcement Learning-Based Vehicle Energy Efficiency Autonomous Learning System"

An ADMM solver for energy management problems in plug-in hybrid electric vehicles

Implementation of Dynamic Programming for Energy Management of Hybrid Electric Vehicle

Driving Range Prediction and Energy Consumption Rate Deviation Classification using ML Models based on Real Electric Vehicle Data

An adaptive hierarchical energy management strategy for hybrid electric vehicles

Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management

Home Energy Management System for Small Prosumers Considering Electric Vehicle Load Scheduling

[IV 2022] A Comparative Study of Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles.

Modern UI tool for modeling solar, energy storage and electric vehicle providing insight regarding how it impact load profile.

Energy-efficient powertrain control of Hybrid Vehicles through traffic jams

An extended version of the VED dataset, which is a large-scale dataset for vehicle energy analysis.

EVLib is a library for the management and the simulation of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid environment.

Optimizes (Maximizes) the velocity profile for a vehicle with respect to physical constraints (e.g., power, force, combined acceleration, ...). Takes into account a variable friction potential betw…

Energy-efficient powertrain control of hybrid vehicles through traffic jams

This graduation project, conducted at Sabanci University, focuses on the analysis of real-world data from electric delivery trucks to understand energy consumption patterns, optimize performance, a…

Driving range prediction by looking at energy consumption rate of Electronic Vehicles using ML regression techniques.

The python code generated random demands of random EV vehicles and household electricity demands. It then plots the graphs between earlier energy peaks and reductions in peaks after the implementat…

An exploration of the Extended Vehicle Energy Dataset

a scalable electric vehicle energy model toolkit for transportation networks

This repository contains a quasi-steady-state lap time simulation implemented in Python. It can be used to evaluate the effect of various vehicle parameters on lap time and energy consumption.

A rule-based energy management strategies for hybrid vehicles using dynamic programming in Matlab

Project achieved in MATLAB/Simulink 2022b, including optimal control algo, prediction algo, DQN training Env. etc.

Predicting the energy consumption of EVs using the RNN and LSTM. Competencies: Machine Learning, RNN, SUMO Simulation. Python Libraries: numpy, pandas, matplotlib, seaborn

Rule based strategy for energy management of Hybrid Electric Vehicle in Simulink

Probabilistic Deep Learningfor Electric-Vehicle Energy-Use Prediction